Discover opportunities for applied AIOrganizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business? Be a part of the program—apply to speak by October 16.

As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. To this end, we first built publishing tools that let people work more efficiently. Now, we build artificial intelligence tools that let people work more intelligently. During this talk we plan to share this evolution with the audience.
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Cognitive Solutions, the application of intelligent technology and services to empower the user to draw insights from data using natural human interaction, is a disruptive force in the US Federal market and is changing the way citizens engage with data.
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Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient.
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We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed.
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Increasingly, companies building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. During this talk, we use a case study from a leading algorithmic trading firm to draw general best practices for building these types of platforms in any industry.
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While Deep Learning has shown significant promise towards model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and over-fit . The presentation explains how a combination of RNNs and Bayesian Network (PGM) can improvise sequence-modeling behavior of RNNs.
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Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you'll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters.
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There is a significant interest in applying deep learning based solutions to problems in medicine and healthcare. This talk will focus on identifying actionable medical problems, and then recasting them as tractable deep learning problems and the techniques to solve them.
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New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.
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Machine learning models are often susceptible to adversarial deception of their input at test time, which is leading to a poorer performance. In this session we will investigate the feasibility of deception in source code attribution techniques in real world environment. This session will present attack scenarios on users identity in open-source projects and discuss possible protection methods.
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Our firm focuses on the application of AI to investment management. Topics covered in this presentation include the application of AI to the problem of asset selection, dealing with low signal-to-noise ratios in financial time series data, the development of real-time macroeconomic indicators from social media data, and the use of heterogeneous compute architectures, specifically GPUs and FPGAs.
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In this talk we will see how machine learning and deep learning techniques can be applied in the field of quantitative finance. We will look at a few use-cases in detail and see how machine learning techniques can supplement and sometimes even improve upon already existing statistical models. We will also look at novel visualizations to help us better understand and interpret these models.
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A case study that details lights-out automation and how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units.
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We propose a framework that unifies Hidden Markov Model and deep learn algorithm (RNN) with modeling components that consider long-term memory and semantics of music (LSTM and Convolution). It takes user original creation as input, modifies the raw scores, and generates musically appropriate melodies.
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